Automate Code Evaluation Pipeline

This example demonstrates a complete pipeline for evaluating code using Large Language Models (LLMs). It integrates LLM output with sandboxed execution and metric calculation to provide a comprehensive code evaluation process.

Use this pipeline when you need to automatically assess the quality, correctness, or performance of code snippets generated by or for an LLM. It's ideal for scenarios where you want to verify code execution in a safe environment before deploying or using it further. Do not use this if you require a simple text-based evaluation without code execution or if your evaluation needs involve complex, multi-stage human review.

The user provides prompts that guide the LLM's code generation or analysis. The pipeline outputs include the LLM's response, the results of the sandboxed code execution, and calculated metrics based on the execution outcomes.

This example utilizes:

• E2B: A platform for running code in secure, ephemeral sandbox environments. This allows for safe execution of potentially untrusted code.

This is for developers, AI engineers, and prompt engineers who need to automate code evaluation and validation within an LLM-driven workflow. It differs from manual code review by providing automated, repeatable testing and metric collection, and from simple LLM code generation by adding a layer of execution verification.